Update app.py

app.py

@@ -125,13 +125,14 @@ def predict(classification_mode, image):
 
 description = """
 <center><img src="https://huggingface.co/spaces/zklee98/SolarPanelAnomaly/resolve/main/images/dronePV_picture.jpg" width=270px> </center><br><br><br><br>
-<center>This program identifies the type of anomaly found in solar panel using an image classification model and the percentage of the affected area using an image segmentation model.</center>
-<center><i>(Models are trained on <a href="https://ai4earthscience.github.io/iclr-2020-workshop/papers/ai4earth22.pdf">InfraredSolarModules</a> dataset, and hence expect infrared image as input)</center></i>
+<center>This program identifies the type of anomaly found in solar panel using an image classification model and </center><br> 
+<center> the percentage of the affected area using an image segmentation model.</center><br>
+<center><i><b>(Models are trained on <a href="https://ai4earthscience.github.io/iclr-2020-workshop/papers/ai4earth22.pdf">InfraredSolarModules</a> dataset, and hence expect infrared image as input)</b></i></center>
 """
 
 gr.Interface(fn=predict,
              inputs= [gr.Dropdown(choices=['Binary Classification', 'Multiclass Classification'], label='Classification Mode:', 
-                                  info='Choose to classify between anomaly and no-anomaly OR between 12 different types of anomalies.'),
+                                  info='Choose to classify between anomaly and no-anomaly (Binary) OR between 12 different types of anomalies (Multi).'),
                       gr.Image(type='pil', label='Input infrared image: ')],
              outputs=[gr.outputs.Label(num_top_classes=3, label='Detected:').style(container=False),
                       gr.Image(type='pil', label=' ').style(height=240, width=144),